Artificial Intelligence Applications in Semiconductor Failure Analysis

Abstract This article provides a systematic overview of knowledge-based and machine-learning AI methods and their potential for use in automated testing, defect identification, fault prediction, root cause analysis, and equipment scheduling. It also discusses the role of decision-making rules, image...

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Bibliographic Details
Published inElectronic device failure analysis Vol. 25; no. 2; pp. 16 - 28
Main Authors Safont-Andreu, Anna, Schekotihin, Konstantin, Burmer, Christian, Hollerith, Christian, Ming, Xue
Format Magazine Article
LanguageEnglish
Published Materials Park Electronic Device Failure Analysis Society 01.05.2023
Subjects
Artificial intelligence
Decisions
Failure analysis
Knowledge bases (artificial intelligence)
Machine learning
Production controls
Prototyping
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Summary:Abstract This article provides a systematic overview of knowledge-based and machine-learning AI methods and their potential for use in automated testing, defect identification, fault prediction, root cause analysis, and equipment scheduling. It also discusses the role of decision-making rules, image annotations, and ontologies in automated workflows, data sharing, and interoperability.
Bibliography:content type line 24
ObjectType-Feature-1
SourceType-Magazines-1
ISSN:1537-0755
2304-8115
DOI:10.31399/asm.edfa.2023-2.p016